Research in Science Education

, Volume 44, Issue 3, pp 461–481 | Cite as

The Development of the STEM Career Interest Survey (STEM-CIS)

  • Meredith W. Kier
  • Margaret R. Blanchard
  • Jason W. Osborne
  • Jennifer L. Albert
Article

Abstract

Internationally, efforts to increase student interest in science, technology, engineering, and mathematics (STEM) careers have been on the rise. It is often the goal of such efforts that increased interest in STEM careers should stimulate economic growth and enhance innovation. Scientific and educational organizations recommend that efforts to interest students in STEM majors and careers begin at the middle school level, a time when students are developing their own interests and recognizing their academic strengths. These factors have led scholars to call for instruments that effectively measure interest in STEM classes and careers, particularly for middle school students. In response, we leveraged the social cognitive career theory to develop a survey with subscales in science, technology, engineering, and mathematics. In this manuscript, we detail the six stages of development of the STEM Career Interest Survey. To investigate the instrument's reliability and psychometric properties, we administered this 44-item survey to over 1,000 middle school students (grades 6–8) who primarily were in rural, high-poverty districts in the southeastern USA. Confirmatory factor analyses indicate that the STEM-CIS is a strong, single factor instrument and also has four strong, discipline-specific subscales, which allow for the science, technology, engineering, and mathematics subscales to be administered separately or in combination. This instrument should prove helpful in research, evaluation, and professional development to measure STEM career interest in secondary level students.

Keywords

STEM interest Instrument Survey Social cognitive career theory STEM careers Confirmatory factor analysis 

References

  1. American Association of State Colleges and Universities (2005, November/December). Strengthening the science and mathematics pipeline for a better America. Policy Matters, 2(11), 1–4. Retrieved from http://www.aascu.org/uploadedFiles/AASCU/Content/Root/PolicyAndAdvocacy/PolicyPublications/STEM%20Pipeline.pdf/.
  2. American College Testing (2011). The condition of college and career readiness 2011. Retrieved January 3, 2012 from http://www.act.org/research/policymakers/cccr11/.
  3. Archer, L. A., Osborn, J., Dillon, J., Willis, B., & Wong, B. (2010). “Doing” science versus “Being” a scientist: Examining 10/11-year-old schoolchildren's constructions of science through the lens of identity. Science Education, 94(4), 617–639.CrossRefGoogle Scholar
  4. Ashby Plant, E., Baylor, A. L., Doerr, C. E., & Rosenberg-Kima, R. B. (2009). Changing middle-school students' attitudes and performance regarding engineering with computer-based social models. Computers & Education, 53(2), 209–215.CrossRefGoogle Scholar
  5. Avery, L. M. (2013). Rural science education: Valuing local knowledge. Theory Into Practice, 52(1), 28–35.CrossRefGoogle Scholar
  6. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs: Prentice-Hall.Google Scholar
  7. Baram–Tsabari, A., Sethi, R. J., Bry, L., & Yarden, A. (2009). Asking scientists: A decade of questions analyzed by age, gender, and country. Science Education, 93(1), 131–160.CrossRefGoogle Scholar
  8. Blanchard, M.R., Albert, J.L., Alsbury, T.L., Williams, B. (2012). NSF ITEST Annual Project Outcomes Report: Innovative technology experiences for students and teachers. STEM Teams: Promoting Science, Technology, Engineering, and Mathematics (STEM) career interest, skills, and knowledge through Strategic Teaming. Arlington: National Science Foundation.Google Scholar
  9. Bowdich, S. (2009). Analysis of research exploring culturally responsive curriculum Hawaii. Paper presented to the Hawaii Educational Research Association Annual Conference, February 7, 2009.Google Scholar
  10. Brotman, J. S., & Moore, F. M. (2008). Girls and science: A review of four themes in the science education literature. Journal of Research in Science Teaching, 45(9), 971–1002.CrossRefGoogle Scholar
  11. Business Europe (2011). Plugging the skills gap: The clock is ticking. Retrieved September 6, 2013 from http://www.businesseurope.eu/Content/Default.asp?pageid=568&docid=28659.
  12. Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming. New York: Routledge.Google Scholar
  13. Capobianco, B. M., Diefes-Dux, H. A., Mena, I., & Weller, J. (2011). What is an engineer? Implications of elementary school student conceptions for engineering education. Journal of Engineering Education, 100(2), 304–328.CrossRefGoogle Scholar
  14. Cataldi, E.F., Green, C., Henke, R., Lew, T., Woo, J., Shepherd, B., and Siegel, P. (2011). 2008–09 Baccalaureate and Beyond Longitudinal Study (BB:08/09): First Look (NCES 2011–236). US Department of Education. Washington: National Center for Education Statistics. Retrieved February 4, 2012 from http://nces.ed.gov/pubs2011/2011236.pdf.
  15. Change the Equation. (2010). Change the Equation: Improving learning in science, technology, engineering, and mathematics. Retrieved December 28, 2012 at www.changetheequation.org.
  16. Clark, L.A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309–319.Google Scholar
  17. Drew, C., (2011, November 4). Why science majors change their minds (It's just so darn hard). The New York Times. Retrieved from http://www.nytimes.com/2011/11/06/education/edlife/why-science-majors-change-their-mind-its-just-so-darn-hard.html?pagewanted=all.
  18. Eccles, J. S. (1994). Understanding women’s educational and occupational choices: Applying the Eccles et al. model of achievement related choices. Psychology of Women Quarterly, 18, 585–609.Google Scholar
  19. Fouad, N. A., Smith, P. L., & Enoch, L. (1997). Reliability and validity evidence for the Middle School Self-Efficacy Scale. Measurement and Evaluation in Counseling and Development, 30(1), 17–31.Google Scholar
  20. Fralick, B., Kearn, J., Thompson, S., & Lyons, J. (2009). How middle schoolers draw engineers and scientists. Journal of Science Education and Technology, 18(1), 60–73.CrossRefGoogle Scholar
  21. Frome, P. M., Alfed, C. J., Eccles, J. S., & Barber, B. L. (2006). Why don't they want a male dominated job? An investigation of young women who changed their occupational aspirations. Educational Research and Evaluation, 12, 359–372.CrossRefGoogle Scholar
  22. Gushue, G. V. (2006). The relationship of ethnic identity, career decision-making self-efficacy and outcome expectations among Latino/a high school students. Journal of Vocational Behavior, 68(1), 85–95.CrossRefGoogle Scholar
  23. Healy, J., Mavromaras, K., Zhu, R. (2011). Consultant report securing Australia's future STEM: Country comparisons. Retrieved September 6, 2011 from http://www.acolasecretariat.org.au/ACOLA/PDF/SAF02Consultants/Consultant%20Report%20-%20Australian%20Labour%20Market.pdf.
  24. Hill, H. C. (2011). The nature and effects of middle school mathematics teacher learning experiences. Teachers College Record, 113(1), 205–234.Google Scholar
  25. Hill, C., Corbett, C., St Rose, A. (2010). Why so few? Women in science, technology, engineering, and mathematics. Washington: American Association of University Women. Retrieved March 22, 2011 at http://www.aauw.org/files/2013/02/Why-So-Few-Women-in-Science-Technology-Engineering-and-Mathematics.pdf.
  26. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.CrossRefGoogle Scholar
  27. Johnson, D. G., & Miller, K. W. (2002). Is diversity in computing a moral matter? SIGCSE Bulletin, 34(2), 9–10.CrossRefGoogle Scholar
  28. Kier, M.W. (2013). Examining the effects of a STEM career video intervention on the interests and STEM professional identities of rural, minority middle school students. Dissertation study, NC State University.Google Scholar
  29. Kitts, K. (2009). The paradox of middle and high school students' attitudes towards science versus their attitudes about science as a career. Journal of Geoscience Education, 57(2), 159–164.CrossRefGoogle Scholar
  30. Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45, 79–122.CrossRefGoogle Scholar
  31. Lent, R. W., Brown, S. D., & Hackett, G. (2000). Contextual supports and barriers to career choice: A social cognitive analysis. Journal of Counseling Psychology, 47(1), 36.CrossRefGoogle Scholar
  32. Lent, R. W., Lopez, A. M., Lopez, F. G., & Sheu, H. B. (2008). Social cognitive career theory and the prediction of interests and choice goals in the computing disciplines. Journal of Vocational Behavior, 73(1), 52–62.CrossRefGoogle Scholar
  33. Marsh, H. W., Hau, K. T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler's (1999) findings. Structural Equation Modeling, 11(3), 320–341.Google Scholar
  34. Masnick, A., Valenti, S., Cox, B., & Osman, C. (2010). A multidimensional scaling analysis of students' attitudes about science careers. International Journal of Science Education, 32(5), 653–667.CrossRefGoogle Scholar
  35. National Academy of Sciences, Global Affairs, & Institute Of Medicine. (2011). Expanding underrepresented minority participation: America's science and technology talent at the crossroads. Washington: National Academy Press.Google Scholar
  36. National Science Board (2010). Science and engineering indicators. Arlington: National Science Foundation (NSB 10-01).Google Scholar
  37. National Science Foundation. (2009). Women, minorities, and persons with disabilities in science and engineering: 2009. Arlington: National Science Foundation. Retrieved January 8, 2013 at http://www.nsf.gov/statistics/wmpd.
  38. Navarro, R. L., Flores, L. Y., & Worthington, R. L. (2007). Mexican American middle school students' goal intentions in mathematics and science: A test of the social cognitive career theory. Journal of Counseling Psychology, 54(3), 320–335.CrossRefGoogle Scholar
  39. Regisford, K. (2012, November 20). Life and work in a global city—The need to improve STEM education. The Recruitment & Employment Confederation. Retrieved November 2, 2012 from http://www.rec.uk.com/press/news/2253.
  40. Schwab, K., & Sala-i-Martín, X. (2012). Insight Report: The Global Competitiveness Report 2012–2013. Geneva: World Economic Forum. Google Scholar
  41. Scott, A. & Martin, A. (2012). Dissecting the data 2012: Examining STEM opportunities and outcomes for underrepresented students in California. Retrieved from May 15, 2012 from http://toped.svefoundation.org/wp-content/uploads/2012/04/Achieve-LPFIstudy032812.pdf.
  42. Skamp, K. (2007). Conceptual learning in the primary and middle years: The interplay of heads, hearts, and hands-on science. Teaching Science, 53(3), 18–22.Google Scholar
  43. STEMconnector® (2012). Where are the STEM students? National Report, Washington. Retrieved September 6, 2012 from http://www.stemconnector.org/sites/default/files/store/STEM-Students-STEM-Jobs-Executive-Summary.pdf.
  44. Stone, D.L., Johnson, R.D., Stone-Romero, E.F., Navas, D. (2005, February). Hispanic American and Anglo American beliefs, attitudes, and intentions to pursue careers in information technology. In E. McChrystal, A.Gujar, & C.Harmon (Chairs), Proceedings. Presentation conducted at the 26th annual Industrial Organizational/Organizational Behavior (IOOB) conference, Indialantic, FL.Google Scholar
  45. Stout, J. G., Dasgupta, N., Hunsinger, M., & McManus, M. A. (2011). STEMing the tide: Using ingroup experts to inoculate women's self-concept in science, technology, engineering, and mathematics (STEM). Journal of Personality and Social Psychology, 100(2), 255.CrossRefGoogle Scholar
  46. TechWomen. (2013). www.techwomen.org.
  47. Thomas, T., & Allen, A. (2006). Gender differences in students' perceptions of information technology as a career. Journal of Information Technology Education, 5, 165–178.Google Scholar
  48. Thompson, B., & Daniel, L. G. (1996). Factor analytic evidence for the construct validity of scores: A historical overview and some guidelines. Educational and Psychological Measurement, 56(2), 197–208.CrossRefGoogle Scholar
  49. Tyler-Wood, T., Knezek, G., & Christensen, R. (2010). Instruments for assessing interest in STEM content and careers. Journal of Technology and Teacher Education, 18(2), 341–363.Google Scholar
  50. US Bureau of Labor Statistics (2010). Occupational outlook handbook, (2010–2011 ed.). Office of Occupational Statistics and Employment Projections. Retrieved January 9, 2012 from http://www.bls.gov/oco/oco2003.htm.
  51. Usher, E. L. (2009). Sources of middle school students' self-efficacy in mathematics: A qualitative investigation. American Educational Research Journal, 46(1), 275–314.Google Scholar
  52. VanLeuvan, P. (2004). Young women's science/mathematics career goals from seventh grade to high school graduation. The Journal of Educational Research, 97(5), 248–268.CrossRefGoogle Scholar
  53. Wells, B., Sanchez, A., & Attridge, J. (2007). Modeling student interest in science, technology, engineering and mathematics. IEEE Summit. “Meeting the growing demand for engineers and their educators,” Munich, Germany.Google Scholar
  54. White House Office of Science and Technology Policy (2012, February 13). Preparing a 21st Century workforce: Science, Technology, Engineering, and Mathematics (STEM) education in the 2013 Budget. Retrieved May 30, 2012 from http://www.whitehouse.gov/sites/default/files/microsites/ostp/fy2013rd_stem.pdf.
  55. Whitfield, A., Feller, R., & Wood, C. (2008). A counselor's guide to career assessment instruments. Broken Arrow: National Career Development Association.Google Scholar
  56. Zeldin, A., Britner, S., & Pajares, F. (2008). A comparative study of the self-efficacy beliefs of successful men and women in mathematics, science, and technology careers. Journal of Research in Science Teaching, 45(9), 1036–1058.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Meredith W. Kier
    • 1
  • Margaret R. Blanchard
    • 2
  • Jason W. Osborne
    • 3
  • Jennifer L. Albert
    • 4
  1. 1.Department of Curriculum and InstructionHoward UniversityWashingtonUSA
  2. 2.Department of Science, Technology, Engineering, and Mathematics EducationNorth Carolina State UniversityRaleighUSA
  3. 3.College of Education & Human DevelopmentUniversity of LouisvilleLouisvilleUSA
  4. 4.Department of Computer ScienceNorth Carolina State UniversityRaleighUSA

Personalised recommendations